class_docstr: Strategy for computing information about data issues related to classification tasks.
code: def get_info(
      data: Data,
      info: Dict[str, Dict[str, Any]],
      issue_name: Optional[str] = None,
  ) -> Dict[str, Any]:
      info_extracted = _InfoStrategy._get_info_helper(
          info=info, issue_name=issue_name
      )
      info = info_extracted if info_extracted is not None else info
      if issue_name in ["label", "class_imbalance"]:
          if data.labels.is_available is False:
              raise ValueError(
                  "The labels are not available. "
                  "Most likely, no label column was provided when creating the Data object."
              )
          # Labels that are stored as integers may need to be converted to strings.
          label_map = data.labels.label_map
          if not label_map:
              raise ValueError("The label map is not available.")
          for key in ["given_label", "predicted_label"]:
              labels = info.get(key, None)
              if labels is not None:
                  info[key] = np.vectorize(label_map.get)(labels)
          info["class_names"] = list(label_map.values())
      return info
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  import numpy
  import pandas
label_desc: Use image node_id 1 for calling the _ClassificationInfoStrategy obj's underlying member method code with example usage: obj.get_info(data, info, issue_name) and returns: info